Presently, there is no clear way to determine if the current body of
biological facts is sufficient to explain phenomenology. In the
biological community, it is not uncommon to assume certain biological
problems to have achieved a cognitive finality without rigorous
justification. In these particular cases, rigorous mathematical
models with automated tools for reasoning, simulation, and computation
can be of enormous help to uncover cognitive flaws, qualitative
simplification or overly generalized assumptions. Some ideal
candidates for such study would include: prion hypothesis, cell cycle
machinery (DNA replication and repair, chromosome segregation,
cell-cycle period control, spindle pole duplication, etc.), muscle
contractility, processes involved in cancer (cell cycle regulation,
angiogenesis, DNA repair, apoptosis, cellular senescence, tissue space
modeling enzymes, etc.), signal transduction pathways, circadian
rhythms (especially the effect of small molecular concentration on its
robustness), and many others.
We believe that the difficulty of biological modeling will become
acute as biologists prepare to understand even more complex systems.

Fortunately, in the past, similar issues had been faced by other
disciplines: for instance, design of complex microprocessors involving
many millions of transistors, building and controlling a configurable
robots involving very high degree-of-freedom actuators, implementing
hybrid controllers for high-way traffic or air-traffic, or even
reasoning about data traffic on a computer network. The approaches
developed by control theorists analyzing stability of a system with
feedback, physicists studying asymptotic properties of dynamical
systems, computer scientists reasoning about a discrete or hybrid
(combining discrete events with continuous events) reactive
systems---all have tried to address some aspects of the same problem
in a very concrete manner. We believe that biological processes could
be studied in a similar manner, once the appropriate tools are made
available.

The goal of this course is to understand, design and create a
large-scale computational system centered on the biology of individual
cells, population of cells, intra-cellular processes, and realistic
simulation and visualization of these processes at multiple
spatio-temporal scales. Such a reasoning system, in the hands of a
working biologist, can then be used to gain insight into the
underlying biology, design refutable biological experiments, and
ultimately, discover intervention schemes to suitably modify the
biological processes for therapeutic purposes. The course will focus
primarily on two biological processes: genome-evolution and
cell-to-cell communication.